AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	logloss	1.95083	1.82
	2_DecisionTree	Decision Tree	logloss	0.25755	7.4
the best	3_Default_Xgboost	Xgboost	logloss	6.95342e-05	36.42
	4_Default_NeuralNetwork	Neural Network	logloss	0.00305178	7.39
	5_Default_RandomForest	Random Forest	logloss	0.355845	6.8
	Ensemble	Ensemble	logloss	6.95342e-05	0.84

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 1_Baseline

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Baseline Classifier (Baseline)

• n_jobs: -1
• num_class: 8
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

1.4 seconds

Metric details

	adolescent	adult	child	elderly	mature adult	senior	teen	young adult	accuracy	macro avg	weighted avg	logloss
precision	0	0.260853	0	0	0	0	0	0	0.260853	0.0326067	0.0680445	1.95083
recall	0	1	0	0	0	0	0	0	0.260853	0.125	0.260853	1.95083
f1-score	0	0.413773	0	0	0	0	0	0	0.260853	0.0517216	0.107934	1.95083
support	1246	4891	3159	1036	3171	2025	1331	1891	0.260853	18750	18750	1.95083

Confusion matrix

	Predicted as adolescent	Predicted as adult	Predicted as child	Predicted as elderly	Predicted as mature adult	Predicted as senior	Predicted as teen	Predicted as young adult
Labeled as adolescent	0	1246	0	0	0	0	0	0
Labeled as adult	0	4891	0	0	0	0	0	0
Labeled as child	0	3159	0	0	0	0	0	0
Labeled as elderly	0	1036	0	0	0	0	0	0
Labeled as mature adult	0	3171	0	0	0	0	0	0
Labeled as senior	0	2025	0	0	0	0	0	0
Labeled as teen	0	1331	0	0	0	0	0	0
Labeled as young adult	0	1891	0	0	0	0	0	0

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 2_DecisionTree

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Decision Tree

• n_jobs: -1
• criterion: gini
• max_depth: 3
• num_class: 8
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

6.9 seconds

Metric details

	adolescent	adult	child	elderly	mature adult	senior	teen	young adult	accuracy	macro avg	weighted avg	logloss
precision	0	1	1	1	1	1	0	0.423232	0.86256	0.677904	0.804391	0.25755
recall	0	1	1	1	1	1	0	1	0.86256	0.75	0.86256	0.25755
f1-score	0	1	1	1	1	1	0	0.594748	0.86256	0.699343	0.821689	0.25755
support	1246	4891	3159	1036	3171	2025	1331	1891	0.86256	18750	18750	0.25755

Confusion matrix

	Predicted as adolescent	Predicted as adult	Predicted as child	Predicted as elderly	Predicted as mature adult	Predicted as senior	Predicted as teen	Predicted as young adult
Labeled as adolescent	0	0	0	0	0	0	0	1246
Labeled as adult	0	4891	0	0	0	0	0	0
Labeled as child	0	0	3159	0	0	0	0	0
Labeled as elderly	0	0	0	1036	0	0	0	0
Labeled as mature adult	0	0	0	0	3171	0	0	0
Labeled as senior	0	0	0	0	0	2025	0	0
Labeled as teen	0	0	0	0	0	0	0	1331
Labeled as young adult	0	0	0	0	0	0	0	1891

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence adolescent (Fold 1)

Dependence adult (Fold 1)

Dependence child (Fold 1)

Dependence elderly (Fold 1)

Dependence mature adult (Fold 1)

Dependence senior (Fold 1)

Dependence teen (Fold 1)

Dependence young adult (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 3_Default_Xgboost

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Extreme Gradient Boosting (Xgboost)

• n_jobs: -1
• objective: multi:softprob
• eta: 0.075
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• eval_metric: mlogloss
• num_class: 8
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

35.8 seconds

Metric details

	adolescent	adult	child	elderly	mature adult	senior	teen	young adult	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	1	1	1	1	6.95342e-05
recall	1	1	1	1	1	1	1	1	1	1	1	6.95342e-05
f1-score	1	1	1	1	1	1	1	1	1	1	1	6.95342e-05
support	1246	4891	3159	1036	3171	2025	1331	1891	1	18750	18750	6.95342e-05

Confusion matrix

	Predicted as adolescent	Predicted as adult	Predicted as child	Predicted as elderly	Predicted as mature adult	Predicted as senior	Predicted as teen	Predicted as young adult
Labeled as adolescent	1246	0	0	0	0	0	0	0
Labeled as adult	0	4891	0	0	0	0	0	0
Labeled as child	0	0	3159	0	0	0	0	0
Labeled as elderly	0	0	0	1036	0	0	0	0
Labeled as mature adult	0	0	0	0	3171	0	0	0
Labeled as senior	0	0	0	0	0	2025	0	0
Labeled as teen	0	0	0	0	0	0	1331	0
Labeled as young adult	0	0	0	0	0	0	0	1891

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence adolescent (Fold 1)

Dependence adult (Fold 1)

Dependence child (Fold 1)

Dependence elderly (Fold 1)

Dependence mature adult (Fold 1)

Dependence senior (Fold 1)

Dependence teen (Fold 1)

Dependence young adult (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 4_Default_NeuralNetwork

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Neural Network

• n_jobs: -1
• dense_1_size: 32
• dense_2_size: 16
• learning_rate: 0.05
• num_class: 8
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

6.7 seconds

Metric details

	adolescent	adult	child	elderly	mature adult	senior	teen	young adult	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	0.999013	1	1	0.999893	0.999877	0.999893	0.00305178
recall	1	0.999591	1	1	1	1	1	1	0.999893	0.999949	0.999893	0.00305178
f1-score	1	0.999796	1	1	1	0.999506	1	1	0.999893	0.999913	0.999893	0.00305178
support	1246	4891	3159	1036	3171	2025	1331	1891	0.999893	18750	18750	0.00305178

Confusion matrix

	Predicted as adolescent	Predicted as adult	Predicted as child	Predicted as elderly	Predicted as mature adult	Predicted as senior	Predicted as teen	Predicted as young adult
Labeled as adolescent	1246	0	0	0	0	0	0	0
Labeled as adult	0	4889	0	0	0	2	0	0
Labeled as child	0	0	3159	0	0	0	0	0
Labeled as elderly	0	0	0	1036	0	0	0	0
Labeled as mature adult	0	0	0	0	3171	0	0	0
Labeled as senior	0	0	0	0	0	2025	0	0
Labeled as teen	0	0	0	0	0	0	1331	0
Labeled as young adult	0	0	0	0	0	0	0	1891

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 5_Default_RandomForest

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Random Forest

• n_jobs: -1
• criterion: gini
• max_features: 0.9
• min_samples_split: 30
• max_depth: 4
• eval_metric_name: logloss
• num_class: 8
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

6.2 seconds

Metric details

	adolescent	adult	child	elderly	mature adult	senior	teen	young adult	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	0.757004	0.996312	0.97696	0.969164	0.982379	0.355845
recall	0.658909	1	1	1	1	1	0.994741	1	0.97696	0.956706	0.97696	0.355845
f1-score	0.794388	1	1	1	1	1	0.85974	0.998153	0.97696	0.956535	0.976194	0.355845
support	1246	4891	3159	1036	3171	2025	1331	1891	0.97696	18750	18750	0.355845

Confusion matrix

	Predicted as adolescent	Predicted as adult	Predicted as child	Predicted as elderly	Predicted as mature adult	Predicted as senior	Predicted as teen	Predicted as young adult
Labeled as adolescent	821	0	0	0	0	0	425	0
Labeled as adult	0	4891	0	0	0	0	0	0
Labeled as child	0	0	3159	0	0	0	0	0
Labeled as elderly	0	0	0	1036	0	0	0	0
Labeled as mature adult	0	0	0	0	3171	0	0	0
Labeled as senior	0	0	0	0	0	2025	0	0
Labeled as teen	0	0	0	0	0	0	1324	7
Labeled as young adult	0	0	0	0	0	0	0	1891

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence adolescent (Fold 1)

Dependence adult (Fold 1)

Dependence child (Fold 1)

Dependence elderly (Fold 1)

Dependence mature adult (Fold 1)

Dependence senior (Fold 1)

Dependence teen (Fold 1)

Dependence young adult (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of Ensemble

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Ensemble structure

Model	Weight
3_Default_Xgboost	1

Metric details

	adolescent	adult	child	elderly	mature adult	senior	teen	young adult	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	1	1	1	1	6.95342e-05
recall	1	1	1	1	1	1	1	1	1	1	1	6.95342e-05
f1-score	1	1	1	1	1	1	1	1	1	1	1	6.95342e-05
support	1246	4891	3159	1036	3171	2025	1331	1891	1	18750	18750	6.95342e-05

Confusion matrix

	Predicted as adolescent	Predicted as adult	Predicted as child	Predicted as elderly	Predicted as mature adult	Predicted as senior	Predicted as teen	Predicted as young adult
Labeled as adolescent	1246	0	0	0	0	0	0	0
Labeled as adult	0	4891	0	0	0	0	0	0
Labeled as child	0	0	3159	0	0	0	0	0
Labeled as elderly	0	0	0	1036	0	0	0	0
Labeled as mature adult	0	0	0	0	3171	0	0	0
Labeled as senior	0	0	0	0	0	2025	0	0
Labeled as teen	0	0	0	0	0	0	1331	0
Labeled as young adult	0	0	0	0	0	0	0	1891

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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